Due to the exchange rate crisis in the CIS region during the fall of USSR era, many smallopen economies have adopted flexible exchange rates in combination with some kind ofmonetary or interest rate mechanism. Volatility of exchange rate in Kazakhstan is significantissue taking account possibility of huge transaction loss induced by volatility exchange rate.Kazakhstan economy highly depends on international

hadgrown up to 2003 but dollar’s exchange rate to Tenge has fallen since 2003 (see Table 1).

There have been numberof attempts to applyneural network (NN)

to the task offinancialmodeling (Cao et al. 2005, Jasic and Wood 2004, Kaastra and Boyd 1995, Lam 2004, Nygren2004). When it comes to performing a predictive analysis, it is very difficult to build onegeneral model that will fit every market. Such models tend to be specific to markets and assetclasses and a general model may not be applicable across markets. Similarly, there may besome temporal changes as well which mean that the models may need to be modifiedovertime in order to preserve their effectiveness

(Zhang, et al, 1998).We develop KZT/USDexchange rate forecast model using new RNN training method and brief results are reportedto show the superior performance of the model, against AR (2) model.

dimension is decomposed into several error functions in lower dimension. Such transformederror function retains the true convex characteristics of the original error function. Thegradient information is evaluated and the training method updates all the network weightparameters saywj,j=1,2,…..m,

by a factor such that improvement in training is noticed. Eachepoch identifiesm

different learning rates along the training directions. Once the NN trainingweights are updated, the error function is evaluated to notice the improvement in errorfunction and the rate of convergence.Various form ofstandardback propagationtrainingmethod and its variant are not self-adaptive. They are heuristictraining method (Ahmed et al2000a; Haykin, 1999; Weir, 1991; and Kamarthi et al., 1999). The training direction,dk

then the algorithm converges globally and the algorithmicmap is closed over

Ω.The following properties are utilized to train the new RNN.

Property 1:Suppose that

f: E

m

→E1

and the gradient of the error function,

▽f

(w), isdefined then there isa directional vectord

such that▽f

(w)T

d<0,

and

f(w +ηd) < f(w)

: (η

Є

(0,

)Ⱐ

> 0),

then the vectord

is a descent direction off

(w), where

i猠慳獵s敤⁡r扩trary灯獩tiv攠ec慬慲⸠

Property2:Letf: E

m

→E1

is a descent function. Consider any training weightw

Є

Em

andd

Є

Em

:d

≠0. Then the directional derivative▽f(w, d)

of the error functionf(w)

inminimumdirectiond

always exists.

Theexpression to update theRNN network weight, wk+1

is

given by

w

k+1=w

k+ηk

dk.Here,η

k

is

defined as a minimization problem of the type:η

k

=

{minf(w +

ηd)};

subject to :ηk

Є

L, where,L

= (η

:η

Є

E1).

3. Results

The following table shows R2

as well the MAD value for the RNN model is better than theAR (2) model. This confirms the superiorityof the KZT/USD exchange model using the newRNN method. The TS vale in both case are within acceptable limits, however, the plot showsthat the TS vale in RNN is asymmetrical on the zero level, hence the overall predictability isgood with RNN model.

developed neural networkmodel(RNN)in foreign exchange forecasting with KZT against USD. TheRNNmodel gives the evidence that there is possibility of extracting information to forecastexchange rate reliably. The evaluation of the model is based on the estimate of meanabsolute error,R2